An improved TLD target tracking algorithm based on Mean Shift

Abstract

Aiming at the problem that the traditional TLD algorithm is not accurate when the target encounters occlusion, a TLD algorithm based on Mean Shift is proposed. When the confidence level of the TLD tracking box is high, the center position of target of the TLD output is used as the starting point of the Mean Shift tracking algorithm. When the confidence level is low, the center position of the target box in the previous frame is used as the iterative starting point for the Mean Shift. The results show that the improved algorithm achieves higher precision, especially for occlusion and target jitter. In order to solve the problem that there are more useless points in the feature points obtained by uniform sampling of TLD algorithm, a more robust Susan corner point is introduced into the TLD tracking module. This algorithm can track the object through the pyramid LK optical flow after selecting the corner. It not only preserves feature points with rich information during the tracking process, but also suppresses the tracking drift caused by more useless points. The results show that this method has high robustness and real — time compared with the original TLD algorithm.